x = Image(filename='icu.JPG', width=600)
display(x)
Image(filename='jeremy.jpg', width=600)
Image(filename='bed_plan.jpg', width=600)
Image(filename='where_jez.jpg', width=600)
Image('news.png')
Image(filename='bed_plan.jpg', width=600)
Real world data for 4000 intensive care patients:
Image('variables.png')
variable='Temp'
unit='(deg C)'
plot_var(example, variable, unit)
variable='HR'
unit='(bpm)'
plot_var(example, variable, unit)
variable='ALP'
unit='(IU/L)'
plot_var(example, variable, unit)
You should build at least one predictive model or classifier.
You should split the data into a training set and a test set. Beware of overfitting, you want performance to generalise.
Test the performance of your model using concepts such as Recall, Precision, and if possible AUC (the area under the ROC curve).
I would recommend starting with X = 5 days.
Are there systematic difference between the training and test sets? What issues might this cause, and what could you do to overcome them?
Think about the different reasons for leaving ICU. Can you account for these in your modelling?
Try developing a different model to predict the scalar length of stay (LOS). Use this new model to predict which patients will have left ICU by day X. How does this compare to your original results?
Are available from: https://physionet.org/challenge/2012/ (use 'set A')